This paper deals with construction of confidence intervals for process capability index using bootstrap method (proposed by Chen and Pearn in Qual Reliab Eng Int 13(6):355–360, 1997) by applying simulation technique. It is assumed that the quality characteristic follows type-II generalized log-logistic distribution introduced by Rosaiah et al. in Int J Agric Stat Sci 4(2):283–292, (2008). Discu...

INTERVAL ESTIMATION OF EXCESS RISK RELATED EFFECTIVE DOSES IN TOBIT MODELS by Jia Wang ADVISOR: Professor Nan Lin December 2009 Saint Louis, Missouri In this thesis we consider interval estimation of excess risk related effective dose (ERED) in dose-response studies using tobit model. Let P (x) be the probability of response at dose level x. Considering the background probability P (0), excess ...

This paper proposes a misspecification-robust iid bootstrap for the generalized method of moment estimators and establishes asymptotic refinements of the symmetric percentile-t bootstrap confidence interval. The paper extends results of Hall and Horowitz (1996) and Andrews (2002). In particular, the proposed method does not involve recentering the moment function in implementing the bootstrap, ...

The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets which are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently Kleiner, Talwalkar, Sarkar, and Jordan (2014) proposed a method called BL...

In this talk, I present some theoretical and empirical properties of the uniform and biased-bootstrap for generalized method of moments (GMM) models. The version of the biased-bootstrap used in this paper is a form of weighted bootstrap with weights chosen to satisfy some constraints imposed by the model. A typical biased-bootstrap resample is obtained by resampling from a member within a pseud...

This paper uses nonparametric methods to estimate the confidence intervals for the mean of asymmetric heavy tailed loss distributions. The nonparametric methods employed are the m out of n bootstrap, subsampling bootstrap, refined bootstrap, empirical likelihood ratio method, and bootstrap calibrated empirical likelihood methods. We evaluate the accuracy and compare the performance of the confi...

The standard error and sampling distribution of robust estimates can, in principle, be estimated using the bootstrap. However, two problems arise when we want to use bootstrap with robust estimates on moderately large data sets: the bootstrap estimates may be unrealiable because the proportion of outliers in many bootstrap samples could be higher than that in the original data set, and the high...

The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets which are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently Kleiner, Talwalkar, Sarkar, and Jordan (2014) proposed a method called BL...

Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover such causal models and has been extended in various directions. An important problem with LiNGAM is that the results are affected by the random sampling of the data as with any statistical method...